TY - JOUR
TI - Impact on the perceived landscape quality through renewable energy infrastructure. A discrete choice experiment in the context of the Swiss energy transition
AU - Salak, B
AU - Kienast, F
AU - Olschewski, R
AU - Spielhofer, R
AU - Wissen Hayek, U
AU - Grêt-Regamey, A
AU - Hunziker, M
T2 - Renewable Energy
AB - This paper examines how mixes (wind, photovoltaic, power lines) of different renewable energy infrastructure (REI) impact people's preferences for various landscape types. This does not only involve the visual character but also meanings that are assigned to these landscapes, which together influence the perceived landscape quality. The research is based on a representative online panel survey of Swiss residents (n = 1062). A discrete choice model (15 choice tasks) was implemented to estimate people's preferences for different REI scenarios across several landscape types. Hierarchical Bayes analysis allowed us to determine preferences of the different respondents, while choice simulation allowed us to estimate preferences for every potential scenario (n = 224) of the discrete choice experiment. While the results show a heterogeneous picture of people's preferences, they also reveal common general patterns. Near-natural, mid/high-elevation landscapes in the Alps are clearly rejected for REI implementation. Landscapes dominated by settlements or intensive agricultural use and landscapes in mountain tourist areas are preferably selected for REI developments. REI scenarios including overhead power lines perform consistently lower than scenarios without power lines. Overall, high preferences for scenarios with low REI indicate that society still lacks awareness of the need for massive REI implementation to achieve a sustainable energy transition.
DA - 2022/06//
PY - 2022
VL - 193
SP - 299
EP - 308
UR - https://www.sciencedirect.com/science/article/pii/S096014812200622X#!
DO - 10.1016/j.renene.2022.04.154
LA - English
KW - Wind Energy
KW - Land-Based Wind
KW - Human Dimensions
KW - Social & Economic Data
KW - Visual Impacts
ER -